Prompt Engineering
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Prompts: Prompt Engineering Guide | Prompt Engineering Guide
- Zero Shot Prompts
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Prompt Engineering (Probably Own note)
- Master the Perfect ChatGPT Prompt Formula (in just 8 minutes)! - YouTube
- Huge List of Coding Prompts: Helpful 201+ Best ChatGPT Prompts for Coding [List UPDATED]
Prompt Roles
Here’s a breakdown of the System, User, and Assistant roles in the context of integrating with OpenAI:
- System Role:
- What it does: The System role is used to provide setup information or context that informs the behavior of the model. This can include instructions or guidelines on how the conversation should proceed.
- When to use it: You use this role to set the stage for the interaction. For example, if you want the model to maintain a formal tone throughout the conversation or if you need to specify rules like avoiding certain topics.
Example:
System: The assistant should always maintain a professional tone and avoid discussing personal opinions on politics.- User Role:
- What it does: This role represents the human user in the conversation. Inputs from the user guide the conversation and prompt responses from the assistant.
- When to use it: Whenever the human user is making a statement or asking a question. This is the most frequent role used in a standard interaction.
Example:
User: Can you explain how to integrate OpenAI’s API with my existing Python application?- Assistant Role:
- What it does: This is the role of the model itself, responding to user inputs based on the context set by the system.
- When to use it: This role is automatically assumed by the model when it replies to the user’s queries or follows the system’s instructions.
Example:
Assistant: Sure, to integrate OpenAI’s API with your Python application, you’ll start by installing the OpenAI Python package using pip...Why not just type everything in one big prompt?
- Clarity and Structure: Using roles helps keep the conversation clear and structured. It delineates who is saying what, which is crucial when designing complex interactions.
- Context Management: It allows the model to maintain and separate different types of context (instructions vs. queries).
- Control and Flexibility: You have better control over how the conversation evolves.
You’re not training the model directly through these interactions; rather, you’re guiding it to respond appropriately within the context you set.
Hope that clears things up!